{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import show\n",
    "import os, sys\n",
    "import subprocess as sp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import of data file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset size: 68\n"
     ]
    }
   ],
   "source": [
    "df=pd.DataFrame([])\n",
    "score_file=r\"all_score.sc\"\n",
    "\n",
    "if (os.path.exists(score_file)):\n",
    "    df1 = pd.read_csv(score_file, sep='\\t').dropna()\n",
    "    df1 = df1[df1.total_score != 'total_score']\n",
    "    df1 = df1.applymap(lambda x : pd.to_numeric(x,errors='ignore'))\n",
    "    df = pd.concat([df,df1])\n",
    "    df['ddg-per-sasa'] = (df['ddG']*1000)/df['sasa']\n",
    "    df['hb-per-sasa'] = (df['hbonds']*1000)/df['sasa']\n",
    "    df['bunsh-per-sasa'] = (df['buried_unsat_hbonds']*1000)/df['sasa']\n",
    "    df['bunsh2-per-sasa'] = (df['buried_unsat_hbonds2']*1000)/df['sasa']\n",
    "\n",
    "print('Dataset size:', df.shape[0])  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is recommended to run an AlphaFold2 (or ColabFold) prediction of all monomeric designs to ensure a proper selection. Uncomment the following lines if you want to load your AlphaFold2 data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#AF2_file='AF2.csv'\n",
    "#dico_rmsd={}\n",
    "#df2 = pd.read_csv(AF_file, sep=';').dropna()\n",
    "#for idx in df2.index:\n",
    "#    name=df2.loc[idx,'design']\n",
    "#    rmsd=df2.loc[idx,'rmsd']\n",
    "#    dico_rmsd[name]=rmsd\n",
    "#df['af2rmsd'] = df['description'].map(dico_rmsd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_score</th>\n",
       "      <th>buried_unsat_hbonds</th>\n",
       "      <th>buried_unsat_hbonds2</th>\n",
       "      <th>sasa</th>\n",
       "      <th>sc</th>\n",
       "      <th>hbonds</th>\n",
       "      <th>ddG</th>\n",
       "      <th>graft_RMSD</th>\n",
       "      <th>array</th>\n",
       "      <th>seed</th>\n",
       "      <th>description</th>\n",
       "      <th>ddg-per-sasa</th>\n",
       "      <th>hb-per-sasa</th>\n",
       "      <th>bunsh-per-sasa</th>\n",
       "      <th>bunsh2-per-sasa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>-520.759</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1302.631</td>\n",
       "      <td>0.680</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-21.675</td>\n",
       "      <td>0.679</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>EEH_1.0_06_S2_0002</td>\n",
       "      <td>-16.639401</td>\n",
       "      <td>9.979802</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.303031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>-581.388</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1169.538</td>\n",
       "      <td>0.686</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-20.210</td>\n",
       "      <td>0.694</td>\n",
       "      <td>157</td>\n",
       "      <td>2</td>\n",
       "      <td>ems_bGwo_45_0001_S2</td>\n",
       "      <td>-17.280328</td>\n",
       "      <td>10.260462</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.565115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>-472.530</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1431.111</td>\n",
       "      <td>0.737</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-28.748</td>\n",
       "      <td>0.605</td>\n",
       "      <td>379</td>\n",
       "      <td>1</td>\n",
       "      <td>HEEH_rd4_0758_S1_0002</td>\n",
       "      <td>-20.087890</td>\n",
       "      <td>9.083852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.397516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>-531.380</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1701.771</td>\n",
       "      <td>0.674</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-20.144</td>\n",
       "      <td>0.557</td>\n",
       "      <td>302</td>\n",
       "      <td>2</td>\n",
       "      <td>4IKGA_S2</td>\n",
       "      <td>-11.837080</td>\n",
       "      <td>8.814347</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.938116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-543.859</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1381.039</td>\n",
       "      <td>0.729</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-22.451</td>\n",
       "      <td>0.565</td>\n",
       "      <td>228</td>\n",
       "      <td>1</td>\n",
       "      <td>1CM2A_S1</td>\n",
       "      <td>-16.256601</td>\n",
       "      <td>10.137295</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.620463</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    total_score  buried_unsat_hbonds  buried_unsat_hbonds2      sasa     sc  \\\n",
       "45     -520.759                  0.0                   3.0  1302.631  0.680   \n",
       "49     -581.388                  0.0                   3.0  1169.538  0.686   \n",
       "39     -472.530                  0.0                   2.0  1431.111  0.737   \n",
       "59     -531.380                  0.0                   5.0  1701.771  0.674   \n",
       "23     -543.859                  0.0                   5.0  1381.039  0.729   \n",
       "\n",
       "    hbonds     ddG  graft_RMSD  array  seed            description  \\\n",
       "45    13.0 -21.675       0.679      3     2     EEH_1.0_06_S2_0002   \n",
       "49    12.0 -20.210       0.694    157     2    ems_bGwo_45_0001_S2   \n",
       "39    13.0 -28.748       0.605    379     1  HEEH_rd4_0758_S1_0002   \n",
       "59    15.0 -20.144       0.557    302     2               4IKGA_S2   \n",
       "23    14.0 -22.451       0.565    228     1               1CM2A_S1   \n",
       "\n",
       "    ddg-per-sasa  hb-per-sasa  bunsh-per-sasa  bunsh2-per-sasa  \n",
       "45    -16.639401     9.979802             0.0         2.303031  \n",
       "49    -17.280328    10.260462             0.0         2.565115  \n",
       "39    -20.087890     9.083852             0.0         1.397516  \n",
       "59    -11.837080     8.814347             0.0         2.938116  \n",
       "23    -16.256601    10.137295             0.0         3.620463  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.sort_values('buried_unsat_hbonds', ascending=True)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Metrics analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "median:1238.7975000000001\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEGCAYAAAB/+QKOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAAsTAAALEwEAmpwYAAAojElEQVR4nO3dd3hc9Zn28e+j3mxLtuVu2bhgisGGCNvUpSymh5Cw1KUEgpMNsG8uQgqbhBCS7KaS3YQs4IAJZEMPBBJM74TqintvcpNkSZbVNTPP+8eMQZiRLcuaOSPp/lyXLp02M3fGTG6dc+b8jrk7IiIie0oLOoCIiKQmFYSIiMSlghARkbhUECIiEpcKQkRE4soIOkBXGjhwoI8ePTroGCLSVVasiP6eMCHYHD3Y3LlzK929ON66HlUQo0ePZs6cOUHHEJGucvLJ0d+vvx5kih7NzDa0t06HmEREJC4VhIiIxKWCEBGRuFQQIiISlwpCRETiUkGIiEhcKggREYlLBSEiInGpIEREJC4VhIhIB40sGYWZpdzPyJJRCfnf26OG2hARSaSyTRu548UVQcf4jJumJ2asKu1BiIhIXCoIERGJSwUhIiJxqSBERCQuFYSIiMSlghARkbhUECIiEpcKQkRE4lJBiIhIXCoIERGJSwUhIiJxJWwsJjObBZwLlLv7xNiyR4Hdg4YUAjXuPjnOY9cDu4AwEHL30kTlFBGR+BI5WN8fgTuBB3cvcPeLd0+b2a+BnXt5/CnuXpmwdCIislcJKwh3f9PMRsdbZ2YGXAScmqjXFxGRAxPUOYgTge3uvqqd9Q68aGZzzWzG3p7IzGaY2Rwzm1NRUdHlQUVEequgCuJS4OG9rD/B3Y8GzgKuN7OT2tvQ3We6e6m7lxYXF3d1ThGRXivpBWFmGcAXgUfb28bdN8d+lwNPAVOSk05ERHYLYg/in4Hl7l4Wb6WZ5ZtZn93TwHRgcRLziYgICSwIM3sYeBeYYGZlZnZtbNUl7HF4ycyGmdns2Oxg4G0zWwh8ADzr7s8nKqeIiMSXyG8xXdrO8qvjLNsCnB2bXgtMSlQuERHpGF1JLSIicakgREQkLhWEiIjEpYIQEZG4VBAiIhKXCkJEROJSQYiISFwqCBERiUsFISIicakgREQkrkTeUU5EpFNGloyibNNGXovNn2IWaJ7eSgUhIimnbNNG7nhxBWNvvgKAO371p4ATRd00fULQEZJKh5hERCQuFYSIiMSlghARkbhUECIiEpcKQkRE4lJBiIhIXIm8J/UsMys3s8Vtlt1mZpvNbEHs5+x2Hnumma0ws9Vm9t1EZRQRkfYlcg/ij8CZcZb/xt0nx35m77nSzNKB3wNnAYcBl5rZYQnMKSIicSSsINz9TaCqEw+dAqx297Xu3gI8ApzfpeFERGSfgjgHcYOZfRQ7BFUUZ/1wYFOb+bLYsrjMbIaZzTGzORUVFV2dVUSk10p2QdwFjAUmA1uBXx/oE7r7THcvdffS4uLiA306ERGJSWpBuPt2dw+7ewT4A9HDSXvaDIxsMz8itkxERJIoqYP1mdlQd98am70AWBxnsw+B8WZ2ENFiuAS4LEkRRaSHc3d2NrayvbaZ8l1NVNW30NgapqElTGsoQkZ6GhlpRnZmGkV5WQzIz2Jgn2yGF+YGHT3pElYQZvYwcDIw0MzKgB8CJ5vZZMCB9cBXY9sOA+5197PdPWRmNwAvAOnALHdfkqicItLzRdzZWtPE6oo61lTUsaspBEB6mtE/L4u87HSK8rLIykgjHHFawxGaWiOUVTeyfNuuj7ct/uIPWLxlJwcP6kNWRs+/jCxhBeHul8ZZfF87224Bzm4zPxv4zFdgRUT2R1NrmMVbdvJR2U52NYVITzNK+udxzKj+DOmXQ//8LNLT9n6viebWMNtqm1hf2cCc6tG8sqyct1dVcsTwfkwaWUhBds+9a0LP/V8mIr1WfXOID9dXsWRLLaGIM6Iwl+PHDuSggfn7/Zd/dmY6owbkM2pAPk/feAI3P7aA+RtrmLuhmnkbqzmqpIgpo/v3yD0KFYSI9BjNoTDzNtQwf1M1oYhz6JC+TB5ZSHGf7C57jWGFuQwrzKWmoYUP1lcxd0M1K7bt4qTxAxk3qADrQXe/U0GISLfn7qwur+P1lRU0tIQ5eFAB08YOoCgvK2GvWZiXxfTDhjBxWD9eX1HB7MXbOHhwAacdMrjH7E2oIESkW9vV1MprKypYV1nPoD7ZnDdpGEP65iTt9YcV5nLJlJHM2VDNe2t2ULGrmbOPGMrAgq7bawmKCkJEuq2V23fxyrJyIu6cOH4gk0cUkraPk86JkGbGlNH9GdYvh+cWb+PRDzdxxuFDGDeoIOlZulLP2A8SkV6lNRzh5WXbeW7xNvrnZ/Gv00ZxdElRIOXQ1oiiPC6bUkJxn2xmL9rKki07A81zoLQHISLdSnVDC39fuJWqhhaOGV3E1IMG7POrqsmUn53BBUcN59mPtvLysnKaWyMcPSresHOpTwUhIt3G+h31PLd4G+lmXHDUcEr65wUdKa7M9DTOmzSMF5Zs463VlYTdOWZ0/6Bj7TcVhIikPHdn3sYa3l5dycCCLM47chh9czODjrVX6WnGmROHkLZ0O++s2UFuVjoTh/ULOtZ+UUGISGpz57UVFSzavJPxgwo4/bDBZKZ3j9OnaWacfuhgmlrCvLqsnLzMdMYUd58T193jXRaRXsnd2VHfwqLNOykdVcRZE4d0m3LYLT3NOPuIoQzqm83sxdvYUtMYdKQO617vtIj0Gk2tYSrrmmlqDXPyhGKOHzew216lnJWRxucnDaMgO4PZi7ZS3xwKOlKHqCBEJOWk5fXjL/PKaAk5AwqymTSiMOhIBywvK4NzjhhKcyjCc4u3EY540JH2SQUhIill284mhlz2M2oaWhlYkEVOZnrQkbpMcZ9sTjtkEJtrGvnHmsqg4+yTCkJEUsamqgYuuudd0gsG8IXJw8nuQeWw2yFD+3LkiH7M31jDqvJdQcfZKxWEiKSEdZX1XHzPu9Q0tLD90e8zvKjn3sHtpPHFDO6bzavLylP6fIQKQkQCt2FHPZfOfI+mUIRHZhxLy9aVQUdKqPQ0Y/phQ2iNOK8sL8c9Nc9HqCBEJFBl1Q1c9of3aQqFeei6qRw2rG/QkZKif34Wx48dwLrKepZurQ06TlwJKwgzm2Vm5Wa2uM2yX5rZcjP7yMyeMrPCdh673swWmdkCM5uTqIwiEqxtO5u4/N73qW1q5f+uncohQ3pHOew2eWQhwwtzeXNlJbWNrUHH+YxE7kH8EThzj2UvARPd/UhgJXDLXh5/irtPdvfSBOUTkQCV72risnvfY0ddCw9eM4WJw7vXMBRdwcyYfthgHOfVFDzUlLCCcPc3gao9lr3o7rvPyLwHjEjU64tI6qqqb+Ff732frTVN3P/lYziqpHuOdtoV+uZmcuyYAWyoamB1RV3QcT4lyHMQ1wDPtbPOgRfNbK6ZzUhiJhFJsJqGaDls2NHAfVeVdstRTrvapBGFDCzI4s2VlbSEIkHH+VggBWFm3wNCwJ/b2eQEdz8aOAu43sxO2stzzTCzOWY2p6KiIgFpRaSr1Da1ctWsD1hdXsfMK0s5btzAoCOlhLQ045QJg6hrDvHB+qp9PyBJkl4QZnY1cC5wubdzwM3dN8d+lwNPAVPaez53n+nupe5eWlxcnIDEItIV6ptDfPn+D1mypZb/vfxo/ulgfV7bGlaYy2FD+zJ/YzU76pqDjgMkuSDM7Ezg28Dn3b2hnW3yzazP7mlgOrA43rYi0j00toS59oEPWbCpht9eehT/fNjgoCOlpOPHDSAzPY03VlakxAnrRH7N9WHgXWCCmZWZ2bXAnUAf4KXYV1jvjm07zMxmxx46GHjbzBYCHwDPuvvzicopIonVHArz1f+by/vrqrjjokmcfcTQoCOlrLysDKYe1J9N1Y1sqIr7N3RSJeyGQe5+aZzF97Wz7Rbg7Nj0WmBSonKJSPK0hiPc8NB83lxZwS++dCTnTx4edKSUd+SIQhaW7eTt1ZWU9M8jLcAhznUltfQoI0tGYWYp9zOyZFTQb03ShcIRvvHoAl5aup3bzz+ci44ZGXSkbiE9zThu7AB21LWwfGuwg/nplqPSo5Rt2sgdL64IOsZn3DR9QtARkioScb79l4949qOtfO/sQ7ny2NFBR+pWxg8qYF7fbN5du4PxgwsCu4ue9iBEpEu5O99/ejFPztvMN08/mOtOGhN0pG7HzDhxXDF1zSEWbKoJLIcKQkS6jLtz+9+X8tD7G/n6yWO54dRxQUfqtoYX5XLQwHzmbqimORQOJIMKQkS6hLvzixdWcP8/1nPN8QfxrTMmdNt7SKeKaWP60xyKsGBjTSCvr4IQkQPm7vzs+eXc9foaLp9awg/OPVTl0AUG9clhzMB85m2qobk1+XsRKggROSDuzo//vox73ljLFdNG8ePzJ6ocutC0MQNoCUWYH8C5CBWEiHSau3PbM0uY9Y91fPn40dx+/uGkpakculJxn2zGFuczP4C9CBWEiHRKJOJ876+LeeDdDXz1pDHceu5h2nNIkKkHRfci5iV5L0IFISL7rTUc4ZuPL+Sh9zdy/Slj+e5Zh6gcEmj3XsTCTTVJHQ68QwVhZsd3ZJmI9Hz1zSGufWAOT83fzLfOmMDN0/VtpWQoHRX9RtPizTuT9pod3YP4XQeXiUgPVlnXzKV/eI9/rK7kF186kutPGadySJIh/XIYUZTLvE3VhCLJ2YvY61AbZnYscBxQbGY3tVnVF0hPZDARSS0bdzRw5az32bqziXv+9XMasjsApaOK+OuCLazYtovDhyX+Ht772oPIAgqIFkmfNj+1wIWJjSYiqWL+xmq+eNc7VDe08tB1U1UOASnpn0dxn2zmbqgmkoT7Rex1D8Ld3wDeMLM/uvuGhKcRkZTz5LwyvvvkIgb3zebhq6YyfnCfoCP1WmZG6aginlu8jbUV9YwbVJDQ1+voaK7ZZjYTGN32Me5+aiJCiUjwQuEIv3xhBfe8uZZpY/pz1+Wfoyg/K+hYvd644gL65WYyZ0MVY4vzE3oOqKMF8ThwN3AvEMyoUSKSNOW1Tdz48HzeX1fFv04r4YfnHR7YkNPyaWlpxlElhby+ooKtO5sYVpibsNfqaEGE3P2uhKUQkZTx9qpKvvHofOqbw/z6Xybxpc+NCDqS7OHQIX15d80O5m+qSYmC+JuZfR14CmjevdDdqxKSSkSSrqk1zK9eWMF9/1jHuOICHr7uaJ1vSFFZGWlMHN6PeRuqqW1sTdjrdHSf8SrgW8A7wNzYz5x9PcjMZplZuZktbrOsv5m9ZGarYr+L2nnsVbFtVpnZVR3MKSKdsGBTDef89i3ufXsdl08t4ekbjlc5pLhJI/qBwcKymoS9Rof2INz9oE4+/x+BO4EH2yz7LvCKu//MzL4bm/9O2weZWX/gh0Ap4MBcM3vG3as7mUNE4qhtauU3L63kgXfWM7hvDn+6dgonji8OOpZ0QJ+cTMYPKmDx5losKzGHmTpUEGZ2Zbzl7v5gvOVt1r9pZqP3WHw+cHJs+gHgdfYoCOAM4KXdh7DM7CXgTODhjuQVkb1zd55esIWfzl5GZV0zl00p4TtnHULfnMygo8l+OGpkESu311FwxD8n5Pk7eg7imDbTOcBpwDw+vWfQUYPdfWtsehsQ74qb4cCmNvNlsWWfYWYzgBkAJSUlnYgjkgSWljJDUuSMnkzhSVeSPfRgfMd6/vbDyzlyRGHQsaQThvTLYWi/HFo/dx7hiJPexUOtd/QQ041t582sEHjkQF/c3d3MDuhyQHefCcwEKC0tTfylhSKd4RHueHFFcC/vTll1Ix+sr6KsupE+ORlMGzOAe686jyP/cH1gueTATT2oP4sf+zURv5Z0AiiIOOqBzp6X2G5mQ919q5kNBcrjbLOZTw5DAYwgeihKRPZDOOKsLq9j3sZqync1k5eVzj8dXMzE4X3JSEsjeopPurNRA/KpW/BcQq5T6eg5iL/xyX9J6cChwGOdfM1niH4r6mex30/H2eYF4D/bfMNpOnBLJ19PpNfZUdfMkq21LN+6i8bWMEV5mZx2yCAOGdKHDF3wJh3U0T2IX7WZDgEb3L1sXw8ys4eJ7gkMNLMyot9M+hnwmJldC2wALoptWwp8zd2/4u5VZvZj4MPYU92uay5E2ufuVNa1sKaijjUVdVTWtZBmcNDAfCYO68eoAXkpcw5Euo+OnoN4w8wG88nJ6lUdfNyl7aw6Lc62c4CvtJmfBczqyOuI9DbuTk1jK1tqGimrjv7UNYcAGNYvh5PGD2TCkD7kZXX2KLJIxw8xXQT8kuh5AAN+Z2bfcvcnEphNpNdzd+qaQ1Q3tFLT0EJ1QyuVdc2U72r++NaTuZnpjCzKZWT/PA4amE9+tkpBukZH/0v6HnCMu5cDmFkx8DKgghDphFA4Qn1LmIaWEA0tYRqaw9Tvnv74d5j65hChyCcnkjPSjP75WUwY3IdBfbMZ0jeHAflZOnwkCdHRgkjbXQ4xO+j4MB0ivU5rOEJ1fQtVDS3UNLTS/8wb+ev8zdQ1h9jVHGr3xvM5mWnkZ2WQl5XOkL455Gen0y83k6K8LIryssjPTlcZSNJ0tCCeN7MX+ORK5ouB2YmJJNL97GxsZVN1A1trmti2s4mqhpZPrc8dewyNrWEK8zIZXpRLfnYG+Vnp5MXKID8rg9ys9C6/0EnkQOzrntTjiF75/C0z+yJwQmzVu8CfEx1OJJVV7GpmVfku1lbUs6M+Wgg5GWkMLczl4MEF9M/Pon9+Fv3yMvn2mefyzQAvlBPpjH3tQfw3sesP3P1J4EkAMzsitu68BGYTSTmhcIRV5XUs2ryTrTubMIPh/XI5afxARg3Ipygvs3sdAkqhIUAk9eyrIAa7+6I9F7r7ojiD8In0WOGIs3jLTj5YV0VDS/RQ0YnjB3LokL7kZqUHHa/zAh4CpD03TZ8QdARh3wVRuJd1ibuNkUiKcHdWbq/jnTWV1DaFGNYvhzMOH8LIolz95S093r4KYo6ZXefuf2i70My+QvSmQSI91q6mVl5dXs76HQ0U98nm/EMGMaq/rkiW3mNfBfEN4Ckzu5xPCqEUyAIuSGAukcC4O0u31vLmykoi7pw0fiCTRhaSpmKQXmavBeHu24HjzOwUYGJs8bPu/mrCk4kEIBSO8PrKCpZsqWVEYS6nHTqIwrysoGOJBKKjYzG9BryW4CwigdrV1Mqzi7ayvbaZY0YXMW3MAO01SK+mQVtEgMq6Zp6av5nWcIRzjhjKuEEFQUcSCZwKQnq9bTub+OuCzWSkGxeVjmRgQXbQkURSggpCerWy6gaeWbiF3Mx0vnj0CPrlZgYdSSRlqCCk19pc08jTC7bQNzeTCyYPpyBHHweRtvSJkF6pYlczzyzcQkFOBl86erhurCMSh4bsll5nZ2Mrf12wmaz0NC6YrHIQaU/SC8LMJpjZgjY/tWb2jT22OdnMdrbZ5tZk55SeqbE1zFPzNxOJOF+YPIy+Oucg0q6k/+nk7iuAyQBmlg5sBp6Ks+lb7n5uEqNJDxeOOLMXbaWuOcSXjh7OAH1bSWSvgj7EdBqwxt03BJxDeoG3V1VSVt3IaYcMYmg/jTUpsi9BF8QlfHKXuj0da2YLzew5Mzu8vScwsxlmNsfM5lRUVCQmpXR7S7bsZEFZDUeVFHLo0L5BxxHpFgIrCDPLAj4PPB5n9TxglLtPAn4H/LW953H3me5e6u6lxcXFCckq3dv22iZeW15BSf88Thg7MOg4It1GkHsQZwHzYgMCfoq717p7XWx6NpBpZvpky35rDoV5bvE2crPSOXPiENJ0z2eRDguyIC6lncNLZjbEYoPum9kUojl3JDGb9ADuzqvLy6ltbOXMiUPIzezGd34TCUAgXwA3s3zgdOCrbZZ9DcDd7wYuBP7NzEJAI3CJu3sQWaX7Wrq1lpXb6zh2zACGF+qktMj+CqQg3L0eGLDHsrvbTN8J3JnsXNJzVNe38PqKCkYU5VI6uijoOCLdUtDfYhLpcpGI8+LS7aSnGWccPkT3dBDpJBWE9DhzN1azrbaJUyYMoiBbw2iIdJYKQnqUzOLRvLd2B+MHFTBhSJ+g44h0ayoI6TFaQhEGnnMTOZnpnDJhUNBxRLo9FYT0GPe8sYaswWM47ZBB5GbpK60iB0oFIT3Cmoo6fvfqauqXvcmYYt1PWqQrqCCk23N3vvfUInIy06h6ZWbQcUR6DBWEdHuPzy3jvbVV3HL2oUTqa4KOI9JjqCCkW6usa+anzy5jyuj+XFw6Mug4Ij2KCkK6tR//fSmNLWH+84sTNRCfSBdTQUi39fqKcp5esIV/O3ks4wbpmgeRrqaCkG6pqTXMD55ezJjifL5+ytig44j0SBqHQLqlu99Yw6aqRh66birZGbrmQSQRtAch3c6mqgbuen0N5x45lON0hziRhFFBSLfzo78tJT3N+N45hwYdRaRHU0FIt/La8nJeXradfz9tPEP76SZAIomkgpBuo6k1zG1/W8KY4nyuOf6goOOI9Hg6SS3dxr1vrWXDjgb+dO0UsjL0t41IogX2KTOz9Wa2yMwWmNmcOOvNzH5rZqvN7CMzOzqInJIayqobuPO11Zw1cQgnji8OOo5IrxD0HsQp7l7ZzrqzgPGxn6nAXbHf0gv95O/LMIzvn3tY0FFEeo1U3k8/H3jQo94DCs1saNChJPneXFnB80u2ccOp4xheqBPTIskSZEE48KKZzTWzGXHWDwc2tZkviy37FDObYWZzzGxORUVFgqJKUJpDYW57ZgmjB+TxlRN1YlokmYIsiBPc/Wiih5KuN7OTOvMk7j7T3UvdvbS4WMeme5pZb69nbWU9P/z84bpiWiTJAisId98c+10OPAVM2WOTzUDb8ZtHxJZJL7F1ZyO/e3UVpx82WPeYFglAIAVhZvlm1mf3NDAdWLzHZs8AV8a+zTQN2OnuW5McVQL0k78vIxxxbtWJaZFABPUtpsHAU2a2O8ND7v68mX0NwN3vBmYDZwOrgQbgywFllQC8taqCZxdt5abTD2Zk/7yg44j0SoEUhLuvBSbFWX53m2kHrk9mLkkNzaEwP3w6emJ6xkljgo4j0msFfR2EyGfc+9Y61lbW88cvH0NOpk5MiwQlla+DkF5oc030xPQZhw/mZJ2YFgmUCkJSyu1/WwLArecdHnASEVFBSMp4bUU5LyzZzo2njtcV0yIpQAUhKaGpNXrF9JjifK47USemRVKBTlJLSpj5pobyFkk1+iRK4DZVNfD711ZzzhFDNZS3SApRQUig3J1bn15Meprx/XN1j2mRVKKCkEA9s3ALr62o4ObpE3SPaZEUo4KQwFTVt/Cjvy1l0shCrjpudNBxRGQPKggJzE/+vpTaxlZ+/qUjSE+zoOOIyB5UEBKIN1ZW8OT8zXz95LEcMqRv0HFEJA4VhCRdfXOI/3hyEWOL87n+1HFBxxGRdug6CEm6X7+4ks01jTzxtWN1lziRFKY9CEmquRuquf+ddVwxbRSlo/sHHUdE9kIFIUlT3xzipscWMLwwl++cdUjQcURkH3SISZLmp7OXsbGqgUeum0ZBtv7TE0l12oOQpHhteTkPvb+R604cw9QxA4KOIyIdkPSCMLORZvaamS01syVm9v/ibHOyme00swWxn1uTnVO6TlV9C9/+y0dMGNyHm04/OOg4ItJBQeznh4Bvuvs8M+sDzDWzl9x96R7bveXu5waQT7pQJOLc/PhCdja06haiIt1M0vcg3H2ru8+LTe8ClgHDk51DkuPet9fy6vJyvn/uoRw+rF/QcURkPwR6DsLMRgNHAe/HWX2smS00s+fMTPef7IbmbazmF8+v4MzDh3DFtFFBxxGR/RTYV0nMrAD4C/ANd6/dY/U8YJS715nZ2cBfgfHtPM8MYAZASUlJ4gLLfqlpaOHGh+YztDCHn194JGYaa0mkuwlkD8LMMomWw5/d/ck917t7rbvXxaZnA5lmNjDec7n7THcvdffS4mLdbCYVhMIRbnhoPuW7mrjz0qPpl5sZdCQR6YQgvsVkwH3AMne/o51thsS2w8ymEM25I3kp5UD813PLeXt1JT/9whFMGlkYdBwR6aQgDjEdD1wBLDKzBbFl/wGUALj73cCFwL+ZWQhoBC5xdw8gq+ynJ+aWcd/b67j6uNFcdMzIoOOIyAFIekG4+9vAXg9Iu/udwJ3JSSRdZc76Kv7jyUUcP24A3z9Htw8V6e50JbV0iZXbd3HtA3MYXpTLnZceTUa6/tMS6e70KZYDtqWmkatmfUB2RhoPXjOFovysoCOJSBdQQcgBqa5v4cpZH1DXFOKBa6Ywsn9e0JFEpItoSE3ptKr6Fi6/9302VjXw4DVTOHSobh0q0pOoIKRTKuuaufwP77N+Rz33XlnKNI3QKtLjqCBkv5XXNnHZve+zubqR+68+huPGxb2GUUS6ORWE7Jfl22q55v4PqWmMjs6qezuI9FwqCOmw11eUc8ND88nPTuexrx7LxOEanVWkJ1NByD65O/e9vY7/em45Ewb34b6rSxnaLzfoWCKSYCoI2asddc1864mPeHV5OdMPG8xvLp5Mvu4nLdIr6JMu7XprVQU3P76Q6vpWbjvvMK46brSG7RbpRVQQMSNLRlG2aWPQMT5jxMgSNm3ckNTXrNjVzE+eXcrTC7YwZmA+9111zGfON6Tq+yUiXUcFEVO2aSN3vLgi6BifcdP0CUl7reZQmIfe38hvXlpJY2uYfz91HF8/ZVzc+0jr/RLp+VQQQms4wuNzyrjz1VVs2dnEcWMHcPv5Exk3qCDoaCISIBVEL1a+q4lHP9jEQx9sZOvOJo4qKeQXF07i+HEDdK5BRFQQAJ+/822GXv1bHv5gI+lpRpoZaWmQbkZmehrZGWlkZ6STlZlGdnoa2Zlp5GSmk5+VQV5WOrlZ6aR1k/9DrWsO8erycmZ/tJVXlm+nNeycOH4g/3nBEZw8oVjFICIfU0EAowfk815tOblZE4lEnIhDKOw0RyK0hltpDkVoDkUIR+Lf1M6A3KxYYWSnkxebzs/OID87nYLsjI/XZaQldwDdUDjCki21vLd2B++u3cE7a3bQEopQ3CebK48dzeVTSxhTrENJIvJZKgjgt5cexe8uO5pbvnbFXrcLRSK0hCI0t0ZobA3T0BKmviVEQ3OYhpYQ9S3R3zvqWmhoCRGvT3Iy0z5VHvlZGdECyY7ujWRnpJEV22PJTN/3X/PuTlNrhF1NrWyrbWJzdSObaxpZtb2O5dtqWbF9F02tEQDGFOdz2ZQSzjlyKEeXFJGepr0FEWmfCmI/ZKSlkZGVRl4WFO1jW3ensTVMfXOY+uYQ9S0h6pvD1DWHPp6vqm+hviXE3u62PfL/PcKx//XKx4e9DMMsuodT1xyirjkUd8+mf34Whw7tw+VTRzFpZCHTDurPoL45B/YGiEivEkhBmNmZwP8A6cC97v6zPdZnAw8CnwN2ABe7+/pk5zwQZkZeVgZ5WRkU98lud7tPFUlLiJZQbC8l9vvlJ57hhOO/TtgdHCLuOJCeZvTJzqBPTiYFOdG9kMF9cxhWmMPwwlz65WbqfIKIHJCkF4SZpQO/B04HyoAPzewZd1/aZrNrgWp3H2dmlwA/By5OdtZk+FSR8NkiefyVmfzy5XsCSCYivV0QtxydAqx297Xu3gI8Apy/xzbnAw/Epp8ATjP9OSwiklTmezsAnogXNLsQONPdvxKbvwKY6u43tNlmcWybstj8mtg2lXGebwYwIzY7AWh7ee9A4DOPSRGpnA1SO5+ydV4q50vlbJDa+Q4k2yh3L463otufpHb3mcDMeOvMbI67lyY5UoekcjZI7XzK1nmpnC+Vs0Fq50tUtiAOMW0GRraZHxFbFncbM8sA+hE9WS0iIkkSREF8CIw3s4PMLAu4BHhmj22eAa6KTV8IvOrJPhYmItLLJf0Qk7uHzOwG4AWiX3Od5e5LzOx2YI67PwPcB/zJzFYDVURLpDPiHnpKEamcDVI7n7J1XirnS+VskNr5EpIt6SepRUSkewjiEJOIiHQDKggREYmrxxWEmf3YzD4yswVm9qKZDWuz7uTY8iVm9kaq5YutP8bMQrHrRVIim5ldHlu+yMzeMbNJyc62j3xmZr81s9Wx9UcHkO2XZrY89vpPmVlhbHmmmT0Qe++WmdktqZIttu5IM3s39plYZGZJH7Brb/li60vMrM7Mbk6VbGZ2upnNjb1nc83s1GRn21u+2LpbYp+JFWZ2RqdewN171A/Qt830vwN3x6YLgaVASWx+UCrli82nA68Cs4ELUyUbcBxQFJs+C3g/ld474GzgOaIjr08LIh8wHciITf8c+Hls+jLgkdh0HrAeGJ0i2TKAj4BJsfkBQHqqvHdt1j8BPA7cnCrZgKOAYbHpicDmZGfbR77DgIVANnAQsKYz/7Y9bg/C3WvbzOYDu8/CXwY86e4bY9uVJztb7HXbywdwI/AXIKWyufs77l4dW/4e0WtXkm4v7935wIMe9R5QaGZDk5ztRXcPxWbbvkcO5Meu58kFWoDaOE8RRLbpwEfuvjC23Q53Dycz2z7yYWZfANYBS5KdC9rP5u7z3X1LbPkSIDc2yGhK5CP6mXjE3ZvdfR2wmugwR/ulxxUEgJn91Mw2AZcDt8YWHwwUmdnrsV3CK1Mpn5kNBy4A7goqV3vZ9nAt0b/WA9FOvuHApjablcWWBeUaPnmPngDqga3ARuBX7l4VVDA+ne1gwM3sBTObZ2bfDjDXbh/nM7MC4DvAjwJN9Im2711bXwLmuXtzkvPsqW2+LvlMdMuCMLOXzWxxnJ/zAdz9e+4+EvgzsHuMpwyiw4efA5wB/MDMDk6hfP8NfMfdI4nIdIDZdj/2FKIF8Z1UzJdo+8oW2+Z7QCiWD6J/tYWBYUR39b9pZmNSJFsGcALRsj0BuMDMTuvqbAeQ7zbgN+5el4hMB5ht9/LDiR7a+Woq5jtgQRw3S+LxuRJgcWz6u8CP2qy7D/iXFMq3jujx6fVAHdHDTF9IhWyx+SOJHsc8OOh/1zjv3T3ApW3WrQCGBpDpauBdIK/Nst8DV7SZnwVclCLZLgEeaDP/A+BbAf17xsv3VpvPRA3Ri2ZvSIVsseUjgJXA8UG8Z/t4724Bbmkz/wJw7H4/d5D/wxL0Zo1vM30j8ERs+lDgFaJ/NeUBi4GJqZJvj23+SDAnqdt770qIHsM8LkX/bc/h0yepPwgg25lEvwRRvMfy7wD3x6bzY9scmSLZioB5sc9DBvAycE6qvHd7bHMbwZykbu+9KyR6EviLyc7UwXyH8+mT1GvpxEnqbj+aaxw/M7MJQATYAHwNwN2XmdnzRL+1ESF6J7vFqZIvRbSX7Vai33D5X4veliPkwYxq2V6+2US/ybQaaAC+HEC2O4l+GF+KvUfvufvXiO5B3G9mS4gW2P3u/lEqZHP3ajO7g+j4aA7Mdvdnk5yt3XwB5IinvWw3AOOAW81s97mw6Z78L7+092+7xMweI1oeIeB678QXEDTUhoiIxNUtT1KLiEjiqSBERCQuFYSIiMSlghARkbhUECIiEpcKQqQLmdlttseoo2Y22swWt5mfEhvyZVVsiItnzeyI5KcV2bueeB2ESMoys8HAY8Bl7v5ObNkJwFhgUZDZRPakghA5QLFxcK4iOjzKJmCumX2O6LAaAC+22fwGosNbvLN7gbu/naysIvtDh5hEDkCsCC4BJhO9mvuY2Kr7gRvdfc+bKx1OdHgLkZSnghA5MCcCT7l7g0fvV/FMbHmhu78Zm/5Tew82s/cteqe5/0l0UJH9pYIQSa4lwMe3RHX3qURHUe0XWCKRdqggRA7Mm8AXzCzXzPoA58WW18ROPkP0fgu7/R642syOa7MsLwk5RfabTlKLHAB3n2dmjxIdWrmc6MioEB1RdpaZOW1OUrv7NjO7GPh57C6C5UAlcHtyk4vsm0ZzFRGRuHSISURE4lJBiIhIXCoIERGJSwUhIiJxqSBERCQuFYSIiMSlghARkbj+Py1cPJuBpQNsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "median:-23.712\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEGCAYAAABiq/5QAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAAsTAAALEwEAmpwYAAAqYElEQVR4nO3deXwV9d3+/9c7CQlZSUJC9rDvOwQQFXcFtYqidbnVutRiv9VuWtv6s4v99u79611ba+16c9elVquiIu6Ka93Y9x0UJCSBECBAIGT/fP/IoY0QIIScM+dkrufjkUeSmcmZi0Pmypw5M58x5xwiIuIfUV4HEBGR0FLxi4j4jIpfRMRnVPwiIj6j4hcR8ZkYrwO0RUZGhuvVq5fXMUS+aP365s8DB3qbQ+QoFi9evNM5l3n49Igo/l69erFo0SKvY4h80VlnNX9+/30vU4gclZltaW26DvWIiPiMil9ExGdU/CIiPqPiFxHxGRW/iIjPqPhFRHxGxS8i4jMqfhERn1Hxi4j4jIpf5CTMnTcPMwu7j4LCnl4/NRLGImLIBpFwVVtbywNz1nsd4wh3XqDxg+TotMcvIuIzKn4REZ9R8YuI+IyKX0TEZ1T8IiI+o+IXEfEZFb+IiM8ErfjN7BEz22Fmqw6b/k0zW2dmq83sV8Fav4iItC6Ye/yPAVNaTjCzs4GpwEjn3FDg10Fcv4iItCJoxe+c+wDYfdjk/wP80jlXG1hmR7DWLyIirQv1Mf4BwCQzm29m/zSzcUdb0Mymm9kiM1tUUVERwogiIp1bqIs/BkgHTgHuBmaambW2oHNuhnOuyDlXlJmZGcqMIiKdWqiLvwSY5ZotAJqAjBBnEBHxtVAX/2zgbAAzGwDEAjtDnEFExNeCNiyzmT0FnAVkmFkJ8FPgEeCRwCmedcCNzjkXrAwiInKkoBW/c+7ao8y6PljrFBGR49OVuyIiPqPiFxHxGRW/iIjPqPhFRHxGxS8i4jMqfhERn1Hxi4j4jIpfRMRnVPwiIj6j4hcR8RkVv4iIz6j4RUR8RsUvIuIzKn4REZ9R8YuI+EzQit/MHjGzHYGbrhw+7y4zc2am2y6KiIRYMPf4HwOmHD7RzAqAC4DiIK5bRESOImjF75z7ANjdyqzfAt8HdMtFEREPhPQYv5lNBUqdc8vbsOx0M1tkZosqKipCkE5ExB9CVvxmlgD8f8BP2rK8c26Gc67IOVeUmZkZ3HAiIj4Syj3+vkBvYLmZfQ7kA0vMLDuEGUREfC8mVCtyzq0Eehz6PlD+Rc65naHKICIiwT2d8ylgLjDQzErM7KvBWpeIiLRd0Pb4nXPXHmd+r2CtW0REjk5X7oqI+IyKX0TEZ1T8IiI+o+IXEfEZFb+IiM+o+EVEfEbFLyLiMyp+ERGfCdmQDSKdyYHaBqLqG4mKS2RjeRVmhhl0iY6iW3wXkuNiiIoyr2OKtErFL3IczjlWle5j7qadLC3ew9LiPWzfV8PTW/cQ26M3r63afsTPmEFafCz56fEUpieQnxZPXEy0B+lFjqTiF2mFc44lxZW8smIbc1aXU7rnIACF6QlM6JPOoOwU+r2dxJqVK7huQiEucFuh2oZG9h6sZ+/BenZU1bKmbB8rSvYSHWX075HE8Lxu5HTripleDYh3VPwiLVTV1DN7aSlPzi9m3fYqYmOiOKN/Jt89fwBnDsgkMznu3wsnxdFUV01GUtwXHiM/7d9fNzQ1sX1vDRvL97NuexXrtleRkRTLxD7d6Z2RqD8A4gkVvwiwa38tj3y8mcc/2UJVbQPD8lL45bThfGlkLklx7d9MYqKiyE9LID8tgdP6ZbChvIrFxZW8vGIbualdOb1fBjnd4jvwXyJyfCp+8bU91XX86f3P+PvcLdQ0NHLRsBymn9GHkQWpHb6u2JgohuV1Y3BOCqvL9jJ/825mLiphZH43TuuXQZdonWQnoaHiF1+qbWjk8U+28Pt3N1JV28DUkbncfnY/+mclB33d0VHGiPxUBmWnMHfTLpZt3cOWXdVcMDRLe/8SEkErfjN7BPgSsMM5Nyww7X7gEqAO+Ay42Tm3J1gZRFrz3vod/PTF1RTvruaMAZncc+EgBuekhDxHbEwUZw7IpE9GIm+tLefZRSWc3i+D0YWpOvYvQRXM15aPAVMOm/YWMMw5NwLYANwTxPWLfEH5vhpuf3IJNz+6kJho4/FbxvP4LeM9Kf2WCtITuG5CIX0zk/jw053MWVNOQ2OTp5mkcwvmHbg+MLNeh02b0+LbecCVwVq/yCGNTY4n5m3h12+up7axibvOH8D0M/uE1Xn1cTHRXDQ8mwWbdzNv824qq+u4ZEQuiSfxxrLI0Xj5W3UL8IyH6xcf+HznAe56djmLt1QyqX8GP586jF4ZiV7HapWZMaFPd7onxfHm6u08t6SEy0fnkdK1i9fRpJPx5DQCM7sXaACePMYy081skZktqqioCF04nyso7BkYfiC8PgoKe57Qv8O55r38C3/3IRvLq/jt1SN5/JbxYVv6LfXrkcTlo/Oorm3kucUlVFbXeR1JOpmQ7/Gb2U00v+l7rnOHrnc8knNuBjADoKio6KjLSccq2VrMA3PWex3jCHdeMLDNy27fW8MPnl/BPzdUMKl/Br+6ckTEnS2TmxrPFWPzmL20jOcWlzBtdB7dD7tQTKS9QrrHb2ZTgO8DlzrnqkO5bvGHOau3M/nBD5i/eRc/nzqUx28ZH3Glf0iP5K5cOTYfgBeWlrL3YL3HiaSzCFrxm9lTwFxgoJmVmNlXgT8AycBbZrbMzP4SrPWLv9Q2NPKzl1cz/e+LKUxP4LVvTeKGib0i/rTI9MRYLh+dR2OTY9aSEvbXNHgdSTqBYJ7Vc20rkx8O1vrEv4p3VXP7P5awsnQvN5/Wix9eOCisztg5WRlJcUwdnccLS0p5YWkpV47NJz628/z7JPR0jbhEtNdWbuPihz5ky64D/M8NY/npJUM7Vekfkp3SlUtG5rC3pp6XV5TpPH85KSp+iUg19Y38aPZKvvHkEvr2SOLVb01i8tBsr2MFVX5aApOHZLFtbw1vr93BMc6NEDkmXR0iEadsz0H+zxOLWV6yl69N6s3dkwcRG+OPfZj+WcmcerCeTz7bRbeELkzs093rSBKBVPwSUeZt2sXtTy6htqGJv1w/linDOvdefmuKeqaxp7qeBZt3k5bQhUHZ3g45IZFHxS8R4+GPNvNfr62lZ/cEZtxQRL8eSV5H8oSZcc6gHuw9WM87a3fQPTHuizeIETkOf7w+lohW39hExpe+x89fWcO5g3rw4u2n+bb0D4mOMi4clk3XLtG8sqKMg/WNXkeSCKLil7C272A9MxdtJWHIGXzvggH85fqxJGvsGgAS42K4eHgOB2obeWPVdpr0Zq+0kYpfwtb2vTU8vXArVTUN7HjuZ9xxTn+ioiL7gqyOlt2tK2cNzKR4dzXzNu3yOo5ECBW/hKWN5VU8t6SE2Jgori4qoGbTYq8jha1hed0YkpPCws8rKd6tkVDk+FT8Elaccyz8fDevrdpOj+Q4ri4qIC0x1utYYe+sgZmkJ8Ty5urtHKjVsA5ybCp+CRuNTY631+7gk892MTArmWmj8zQ0QRt1iY7iwuHZ1DY0MWdNOaBDYnJ0Op1TwkJNfSOvrtxGSeVBxvdO55Te6RE/wFqoZSTFcWb/TN5dv4OUCdO8jiNhTHv84rk91XXMXLSVsj0HuWBIFhP7dFfpt9OwvBT690gi9YyvsHhLpddxJEyp+MVTZXsOMnNRCQfrGpk2Ot/zG59HOjPj3ME9aNhXwbeeWqox/KVVKn7xzPrtVcxaUkpcTBRXjSsgLy0yb5gSbuJiotn50q/Yvq+Gn7202us4EoZU/BJyzjnmb97FG6u3k92tK1eNKyAtQWfudKS6bRv45jn9mLW0lFdXbPM6joSZYN6B6xEz22Fmq1pMSzezt8xsY+BzWrDWL+Gpoan5rJN5m3YzKDuZy0bnEt9FZ+4Ew+1n92NkQSr3zl5J+b4ar+NIGAnmHv9jwJTDpv0QeMc51x94J/C9+MTB+kZmLy1j3fYqJvbpzgVDsoiJ0ovOYOkSHcVvrxpJTX0jdz+3QuP3y78Ebatzzn0A7D5s8lTgb4Gv/wZcFqz1S3iprK5j5sKtbN9Xw5Sh2YzX6Zoh0ScziXsvGswHGyp4Yn6x13EkTIT6PP4s59yhA47bgayjLWhm04HpAIWFhSGIJsFSWnmQV1aUYWZMG51Hbmo73sS1qLD7Q/Ge1wHa6PpTevLW2h384tU1nNa3O30y/T2yqbSx+M3sNOfcx8ebdiKcc87Mjvra0zk3A5gBUFRUpNeoEWrttn28vbacbvFduHRkLqntfRPXNfHAnPUdG+4k9f3eDXy2YoHXMY7LzLj/yhFMfvADvjtzOc9/fSIx0TrE5mdt/d//fRunHU+5meUABD7vaMdjSARwzjH3s13MWVNObmo8VxUVtL/05aRlpXTlF5cNZ/nWPfzxvc+8jiMeO+Yev5lNBE4FMs3szhazUoD2nIrxEnAj8MvA5xfb8RgS5hoam3hrbTkbyvczJCeFcwb1IFrDKXvu4hE5vLUml4fe3chZAzMZWZDqdSTxyPH2+GOBJJr/QCS3+NgHXHmsHzSzp4C5wEAzKzGzr9Jc+Oeb2UbgvMD30olU1zUwa2kpG8r3c2rf7pw3WKUfTn42dRg9kuO4c+YyanTXLt865h6/c+6fwD/N7DHn3JYTeWDn3LVHmXXuiTyORI7dB+p4aXkZ+2sbuGhYNv2zkr2OJIfpFt+FX105ghseXsD9b67nx18a4nUk8UBbz+qJM7MZQK+WP+OcOycYoSTybN1dzasrtxFlxhVj8sjppuEXwtWk/pnccEpPHvl4M+cPyeKUPt29jiQh1tbifxb4C/BXQK8P5QtWl+3l3XU7SE2IZerIXFLidU/ccHfPRYP4cGMF33t2Oa9/e5LuY+wzbT2rp8E592fn3ALn3OJDH0FNJmHPOcfHn+7k7bU7yE9L4Kqx+Sr9CJEQG8NvrhpJ2Z6D/Ocra72OIyHW1uJ/2cy+YWY5gfF20s0sPajJJKw1NDbx+qrtLNpSybDcFC4dmUucxtyJKGN7pnPbmX15ZtFW3l1X7nUcCaG2Huq5MfD57hbTHNCnY+NIJDhQ28ArK7axfV8Np/fLYExhathdVStt853z+vPeuh384PmVzPlOmu5v7BNt2uN3zvVu5UOl70M799fyzKKt7Nxfy8XDcxjbM02lH8HiYqJ54KpR7Kmu40cvrjr+D0in0NYhG77S2nTn3OMdG0fC2eadB3h91TZio6O4Ymw+2SldvY4kHWBIbgrfOW8A97+5nslDy7h0ZK7XkSTI2nqoZ1yLr7vSfC7+EkDF7wPOOZZu3cNHG3eSkRzHJSNydBZIJ3PbGX14e205P569igm908nSH/VOra2Her7Z4uNrwBiar+iVTq6xyfHuuh18uHEnfTIT+fLYfJV+JxQTHcVvvjyS2oZGfvC8xu7v7No7RN8BoHdHBpHw03zjlFJWle1jXK80Lh6eQxeN6thp9clM4p4LB/P++gqeWrDV6zgSRG09xv8yzWfxQPPgbIOBmcEKJd771/ALNQ1MHpLFoJwUryNJCNxwSk/mrNnOf766htP7ZVDYPcHrSBIEbT3G/+sWXzcAW5xzJUHII2Fgy64DvLZqO9FmTBvTzhunSESKijLuv3Ikk3/7Ad97djlPTT9Fg+x1Qm09xv9PYB3NI3OmAXXBDCXecM6RMv4KXlxWRnJcDNeMK1Dp+1Buajw/vXQoCz7fzSMfbfY6jgRBm4rfzK4CFgBfBq4C5pvZMYdllshyoLaBO/6xlLSzb6ZfjySuKirQ8As+dsWYPC4YksX9b65nTdk+r+NIB2vrO3X3AuOcczc6574CjAd+HLxYEkqf7zzAtD99wuurtlH53iNcOCyb2Bi9ietnZsYvrxhBakIXvvnUEg7WaWzGzqStW3eUc67lbRJ3ncDPHsHMvmtmq81slZk9ZWY6adgj763fwaV/+Ijyqhr+dst49i2YpStxBYD0xFh+e/UoNu08wP99ZY3XcaQDtbW83zCzN83sJjO7CXgVeK09KzSzPOBbQJFzbhjNZwld057HkvZranI89M5GbnlsIXlpCbx8x+lM6p/pdSwJM6f1y+C2M/ry1IJi3li1zes40kGOd8/dfkCWc+5uM5sGnB6YNRd48iTXG29m9UACUHYSjyUnaOf+Wr77zDI+3LiTy0bl8v9PG0F8rEbWlNbddcEA5n62kx88v5IR+al6w78TON4e/4M0318X59ws59ydzrk7gRcC806Yc66U5tNDi4FtwF7n3JzDlzOz6Wa2yMwWVVRUtGdV0oq5n+3iot99yILNu/nltOH89upRKn05pi7RUTx07WgaGpv4zjPLaGzSVb2R7njFn+WcW3n4xMC0Xu1ZoZmlAVNpvvI3F0g0s+tbWccM51yRc64oM1OHIE5WU5Pj9+9s5Lq/ziMpLobZt5/GNeMLdTxf2qRn90R+ftkwFmzezUPvbPQ6jpyk413AlXqMee19vXcesNk5VwFgZrOAU4En2vl4chwtD+1MHZXLLy4fTlJcW6/dE2k2bUw+H3+6i4fe3cjowlTOGtjD60jSTsfb419kZl87fKKZ3Qq099aLxcApZpZgzbub5wK691uQzFm9nSkP/vvQzoNXj1LpS7v952XDGJiVzHeeWUZJZbXXcaSdjlf83wFuNrP3zew3gY9/Al8Fvt2eFTrn5gPP0Tys88pAhhnteSw5ur0H67lz5jKm/30xPZLjePEOHdqRkxcfG82frx9LY6PjG08uobZB5/dHomPu+jnnyoFTzexsYFhg8qvOuXdPZqXOuZ8CPz2Zx5Cj++eGCn7w3Aoq9tfyrXP7c8fZ/XRBlnSY3hmJ/Pqqkdz298X835fX8IvLh3sdSU5Qm17zO+feA94LchY5SftrG/jFq2t5akEx/XskMeMrYxmRn+p1LOmEJg/N5rYz+vA/H2xiTGEaV4zN9zqSnAAd7O0k5m3axfeeXU7pnoPcdmYfvnveALp20WmaEjx3Tx7I0q17uHf2SgZmJzMsr5vXkaSN9Po/wh2sa+RnL6/mmhnziIkynr1tIvdcOFilL0EXEx3FH/9jDOkJsdz6t0Xs2FfjdSRpIxV/BFtSXMnFD33Iox9/zk2n9uK1b0+iqFe617HERzKT4/jfG4vYe7Cer/19MTX1erM3Eqj4I1BtQyP//cY6rvzzJ9Q2NPGPWydw36VDSYjVkTsJvaG53XjwmlEs37qHu5/T/XojgZoiwqwq3ctdM5ezvryKa8YVcO/Fg3Xzc/Hc5KHZ3D15IPe/uZ6CtHi+P2WQ15HkGFT8EaK+sYk/vvcpf3j3U9ITY3n05nGcrSsnJYx846y+lFRW86f3PyMrpSs3ntrL60hyFJ2++AsKe1KytdjrGEfILyhka/GWNi27obyKO2cuY1XpPi4blct9lw4lNSE2yAlFToyZ8fOpw6ioquO+l1eTlRLHlGE5Xsdqk3DtCTixrmirTl/8JVuLeWDOeq9jHOHOCwYed5nGJsf/friJB+ZsILlrDH+5fixThmWHIJ1I+8RER/H7a0dz3V/n8a2nl/HYTV04tV+G17GOK1x7AtrWFSdKb+6GqU0V+/nyXz7hl6+v45xBPZjz3TNU+hIR4mOjefjGcfTunshX/7aIhZ/v9jqSHEbFH2aamhyPfbyZix76kM8qDvC7a0bx5+vH0D0pzutoIm2WlhjLE7dOICe1Kzc/upClxZVeR5IWVPxhpKSymuv+Op/7Xl7DxD7dmfPdM5g6Kk8Dq0lEykyO4x+3nkL3pFi+8sgClm3d43UkCVDxhwHnHM8vLuHCBz9kZelefnXFCB65aRxZKboHvUS27G5d+cfXTiEtIZbr/ncen3y20+tIgorfc7sP1PGNJ5dw17PLGZyTwuvfnsRV4wq0ly+dRl5qPM9+fSJ5afHc9OhC3l5T7nUk31Pxe+j99TuY/OAHvL22nB9eOIinpp9CQXqC17FEOlxWSleemT6RQdnJ3PbEYmYu2up1JF9T8XugvrGJ9PO/zk2PLiQ9IZYXbz+dr5/Zl+go7eVL55WWGMuTt07g1L7d+f5zK/jvN9bRpBu3e8KT4jezVDN7zszWmdlaM5voRQ4v7NpfyzMLt5I0+iK+Nqk3L95xGkNyU7yOJRISyV278MhN4/iPCYX8+f3PuP0fSzhYp4HdQs2rPf7fAW845wYBI/HBPXedc6wu28vTC7dSXdfIjpk/4d6Lh2j4ZPGdLtFR/OKyYfzo4sG8sXo7l//pYzZV7Pc6lq+EvPjNrBtwBvAwgHOuzjm3J9Q5QqmuoYk3V5fz9todZHfrynUTCqn5fJnXsUQ8Y2bcOqkPj908nvJ9NVzy+494ZUWZ17F8w4s9/t5ABfComS01s7+aWeLhC5nZdDNbZGaLKioqQp+yg+zaX8tTC4vZUF7FxD7duXx0HolxnX6kDPGaRWFmYfdRUNjzCzHPHJDJq9+axIDsZO74x1LumbWC/bUNHj1p/uFFA8UAY4BvOufmm9nvgB8CP265kHNuBjADoKioKCLfAdpYXsVba8vpEh3FtDF55KfpjB0JEdcUlmPPtDbuTG5qPM9Mn8hv3lrPjA828eHGndx/5Ugm9u3uQUJ/8GKPvwQocc7ND3z/HM1/CDqNJuf46NOdvLZqO90T47h2XKFKX+QYYmOiuOfCwTx720Sio4xr/3ce976wkj3VdV5H65RCXvzOue3AVjM79Kf/XGBNqHMES019Iy8uK2PxlkqG5aZwxdg8krrq0I5IWxT1Suf1b0/i5tN68dSCYs75zT+ZuXCrTvvsYF6d1fNN4EkzWwGMAv7LoxwdqrK6jqcXbqW08iDnDurBuYOziInSpRIiJyIhNoafXjKUV745id4ZiXz/+RVM/ePHfLgxct/rCzee7Io655YBRV6sO1hKKqt5ZcU2osyYNiaP3NR4ryOJRLQhuSk8e9tEZi8r5TdzNnDDwws4rV937jx/IGN7pnkdL6LpGEQHWFO2j3fWldMtvgtTR+XRLV73wBXpCFFRxrQx+Vw8Iocn5xXzh/c+5Yo/f8K4XmncdkZfzhnUgyhd8X7CVPwnwTnH3E27WPh5JQVp8Vw8PIc4XZAl0uHiYqK55fTeXD2ugGcWbuXhjzZz6+OL6JuZyPQz+jB1VJ4uhjwBOgDdTo1NjrfWlrPw80qG5qYwdVSeSl8kyBLjYrjl9N68f/dZ/O6aUcTGRPOD51cy7hdv85MXV7GmbJ/XESOC9vjbob6xiddWbuPzXdWc0jud8b3TNYyySAh1iY5i6qg8Lh2Zy9xNu3hm4VaeXriVx+duYWR+N75cVMBFw3NIT4z1OmpYUvGfoJr6Rl5aXsa2vTWcPTCTEfmpXkcS8S0z49S+GZzaN4P7DtQxe1kpTy/Yyo9mr+K+l1ZzxoBMLh2Zy/lDsnTFfAt6Jk5AVU09s5eVsbe6nouGZ9O/R7LXkUQkIC0xlptP681Np/ZizbZ9vLS8jJeXlfHuuh107RLFeYOzuHRkLmcOzCQuxt+HZVX8bbT3YD2zlpRQU9/E1FG5J3/DlMBYKiLSscyMobndGJrbjR9MHsTi4kpeWlbGqyu38cqKbaR0jWHKsGwuGZnLxD7diYn231udKv42qKyuY9aSUuobm5g2Jq9j7oUbQWOpiESqqChjXK90xvVK5yeXDOHjT3fy8vJtvLZyOzMXlZCRFNt8Nl7eYJxzvtkZU/Efx679tcxaWopzcMWYfDKT47yOJCLt0CU6irMG9uCsgT34Rf0w3l+/g5eWl/H0wq1kX38/j37yOQOykhmYldzpt3MV/zFUVNXywtJSzOCKMXl0T+rcvwwiftG1SzRThuUwZVgOVTX15BRNpueNP2ZpcSWLt1TSIzmOobkpDMxO7pTvB6j4j6J8Xw2zl5YSExhSOS1Bp4WJdEbJXbtwYPV7XDbqLxysa2RDeRWryvby3voKPty4k/5ZSYzISyW7Wwcc4g0TKv5WbNt7kNnLyoiLieKKMfkagkHEJ+JjoxlZkMqI/G6UV9WyunQv68urWLutitxuXRldmEafzESiIvy9ABX/YbbvrWH20jLiY6OZNiaPlK4qfRG/MTOyU7qSndKVSf0zWV22l2Vb9/Dqym10i+/C6IJUhuamROwZQSr+Fiqqapm9rJSuXaK4YkweySp9Ed+LjYlidGEaI/NT+axiP0uK9/D+hgoWfr6bcb3SGZqXEnHDr6v4A3btb34jt0t08+Edlb6ItBQVZfTPSqZ/VjIlldXM3bSL9zdUsGhLJRN6pzM4J4XoCBkp1LM/U2YWHbjZ+iteZTiksrqOWUtLiTKaD+/omL6IHEN+WgJXjsnnslG5JMZF8866HTwxbwubdx7wOlqbeLnH/21gLZDiYYbAFbmB8/TH5uvsHRFpEzOjZ/dECtMT2LzzAB99upOXlpfRs3sCZ/bPJC2MB4jzZI/fzPKBi4G/erH+Q/bV1PP8khIaGpu4fHSeRvITkRNmZvTJTOK6CT2Z1D+DbXtqeGL+Fj7YWEFtQ6PX8Vrl1R7/g8D3gaOOcmZm04HpAIWFhR0eYH9tA7OWlFLb0MS00Xmd/ko9kbDQiceoio4yxhSmMTArmbmbdrG0eA8by/dzzqAe9M5I9DreF4S8+M3sS8AO59xiMzvraMs552YAMwCKiopcR2aormtg1pISqusauHx0B429IyLH54MxqhLjYjhvcBZDc1N4e23zsBCDspM5Y0Am8WFysyYvDvWcBlxqZp8DTwPnmNkToVr5wfpGZi0tpaqmgakj88jpppuii0jHy+kWz7XjCxjfK50N5VX8fe4WNpZXeR0L8KD4nXP3OOfynXO9gGuAd51z14di3bX1jcxeWsqe6nouGZlLXppKX0SCJyYqiol9u3PNuEKSu8bw2qrtvLl6u+fH/iPrqoOTUNfQxOxlZezcX8vFw3MoPNnx9EVE2igzOY6riwqY0Dud9dur+Mf8Ysr2HPQsj6fF75x73zn3pWCvp76xiReXl1JeVcNFw3PC7o0WEen8oqKMU/p058qx+QA8t6SEeZt20dTUoW9hti1LyNcYYhYTy8vLy9i2p4YpQ7Ppm5nkdSQR8bHc1Hj+Y0IhA7OSmb95N88vLWF/bUNIM3Tq4q9raCLjsnvYWnmQ84dkMSBL98gVEe/FxUQzeWg2k4dmsWNfLU8tKKaksjpk6+/Uxf/j2atI6DuOcwf1YHCOpxcIi4gcYVB2CteMKyAuJopZS0pZ+PlunAv+oZ9OXfxfndSbXa8/xLC8bl5HERFpVfekOK4ZV0j/Hkl88tkuXl6xjZr64J7106mLf0BWMvtXzPE6hojIMcXGRDFlWDZnDshky64DPLWgmB37aoK2vk5d/CIikcLMGFWQypVj82lyMHNxCWu37QvKulT8IiJh5NAVvzkpXZmzppyEgad1+DpU/CIiYSYhNobLR+dx5oBMqjfO7/DHV/GLiIShqKjmQz80dfw5/ip+ERGfUfGLiPiMil9ExGdU/CIiPqPiFxHxGRW/iIjPhLz4zazAzN4zszVmttrMvh3qDCIifhbym60DDcBdzrklZpYMLDazt5xzazzIIiLiO17cc3ebc25J4OsqYC2QF+ocIiJ+5ekxfjPrBYwGjrgm2cymm9kiM1tUUVER8mwiIp2VZ8VvZknA88B3nHNHDEHnnJvhnCtyzhVlZmaGPqCISCflSfGbWReaS/9J59wsLzKIiPiVF2f1GPAwsNY590Co1y8i4nde7PGfBtwAnGNmywIfF3mQQ0TEl0J+Oqdz7iPAQr1eERFppit3RUR8RsUvIuIzKn4REZ9R8YuI+IyKX0TEZ1T8IiI+o+IXEfEZFb+IiM+o+EVEfEbFLyLiMyp+ERGfUfGLiPiMil9ExGdU/CIiPqPiFxHxGRW/iIjPeHXP3Slmtt7MPjWzH3qRQUTEr7y452408EfgQmAIcK2ZDQl1DhERv/Jij3888KlzbpNzrg54GpjqQQ4REV8y51xoV2h2JTDFOXdr4PsbgAnOuTsOW246MD3w7UBg/UmuOgPYeZKPEQqRkFMZO0YkZITIyKmMrevpnMs8fGLIb7beVs65GcCMjno8M1vknCvqqMcLlkjIqYwdIxIyQmTkVMYT48WhnlKgoMX3+YFpIiISAl4U/0Kgv5n1NrNY4BrgJQ9yiIj4UsgP9TjnGszsDuBNIBp4xDm3OgSr7rDDRkEWCTmVsWNEQkaIjJzKeAJC/uauiIh4S1fuioj4jIpfRMRnOn3xm9n9ZrbOzFaY2Qtmltpi3ggzm2tmq81spZl1DbeMgfmFZrbfzL7nRb4WOVrNaWbnm9niwHO42MzOCbeMgXn3BIYJWW9mkz3M+OXA71yTmRW1mN7FzP4WeB7Xmtk94ZYxMC8stpvj5QzM93zbOcb/t2fbTacvfuAtYJhzbgSwAbgHwMxigCeArzvnhgJnAfXhlLGFB4DXQ57qSEfLuRO4xDk3HLgR+LtH+eDo/99DaD6DbCgwBfhTYPgQL6wCpgEfHDb9y0Bc4HkcC9xmZr1CnO2QVjOG2XYDR38uDwmHbedoGT3bbsL2Aq6O4pyb0+LbecCVga8vAFY455YHltsV6myHHCMjZnYZsBk4EOJYRzhaTufc0hbTVwPxZhbnnKsNZb5AlqM9l1OBpwOZNpvZpzQPHzI3xBFxzq0FMLMjZgGJgXKNB+qAfaFNFwhy9Ixhs90E1n+0nGGz7Rwto5fbjR/2+Fu6hX//9R8AODN708yWmNn3PczV0r8ymlkS8APgZ54mal3L57KlK4AlXpR+K1pmzAO2tphXEpgWTp6juaS2AcXAr51zu72NdIRw3W6+IMy3ndaEdLvpFHv8ZvY2kN3KrHudcy8GlrkXaACeDMyLAU4HxgHVwDtmttg5904YZbwP+K1zbn9rezRhlPPQzw4F/pvmvcKwzBgqbcnYivFAI5ALpAEfmtnbzrlNYZQxpNsNtDvnfYRw22lnxkM/G5LtpqVOUfzOufOONd/MbgK+BJzr/n3hQgnwgXNuZ2CZ14AxQFB+gduZcQJwpZn9CkgFmsysxjn3h2BkPImcmFk+8ALwFefcZ8HKdxIZQzpUyPEyHsV/AG845+qBHWb2MVAEBKX425kxpNsNtDtnSLeddmYM6XbTUqc/1GNmU4DvA5c656pbzHoTGG5mCYFjqmcCa8Ipo3NuknOul3OuF/Ag8F/BLP3jOVrOwJkzrwI/dM597FG8Q1mO9v/9EnCNmcWZWW+gP7DAi4zHUAycA2BmicApwDpPEx0pbLabYwm3bac1Xm43nb74gT8AycBbZrbMzP4C4JyrpPkd/4XAMpqPr70aThnD0NFy3gH0A34SmL7MzHqEU8bAsCAzaS6pN4DbnXONXgQ0s8vNrASYCLxqZm8GZv0RSDKz1TT/Xj7qnFsRThnDbLs51nMZNo6R0bPtRkM2iIj4jB/2+EVEpAUVv4iIz6j4RUR8RsUvIuIzKn4REZ9R8UunYmb3HT4So5n1MrNVXmUSCTcqfpEg8HDkT5HjUvFLxDOze81sg5l9BAwMTBtrZsvNbDlwe4tlE8xsppmtsebx+udb6+O432RmL5rZ+2a20cx+2mLe9Wa2IHDBzf8cKnlrHvf9N4F1Tjzs8X4ZWOcKM/t1YNolgfUvNbO3zSwrMP3MFhf0LDWzZDNLMrN3AgOjrTSzqUF4KsUnOsVYPeJfZjaW5nH2R9H8+7wEWAw8CtzhnPvAzO5v8SPfACqdc0PMbBjNV58ezXhgGM2DkS00s1dpHj3zauA051y9mf0JuA54HEgE5jvn7josY3fgcmCQc87Zv28O8xFwSmDarTQPNXEX8D2aryz+2JpHmawJLH+5c26fmWUA88zsJacrMKUdVPwS6SYBLxwal8fMXgpMT3XOHbrxxd+BCwNfnw78DsA5t8rMjjUkwluHxps3s1mBn22g+SYpCwOjPsYDOwLLNwLPt/I4e2ku74fN7BXglcD0fOAZM8sBYmkeOx7gY+ABM3sSmOWcKzGzLsB/mdkZQBPNQ0pnAduP9eSItEaHekT413gqhw6vHDr0c/jetAMM+JtzblTgY6Bz7r7A/JpD4/9Y83j1y8zsr865BppfPTxH86ihbwSW/z3wh8AdmG4DugI4534J3ErzH5WPzWwQza8qMoGxzrlRQPmh5UVOlIpfIt0HwGVmFm9mycAlgel7zOz0wNfXtVj+Y+Aq+NftGIcDOOdeaFHmiwLLnm9m6WYWD1wW+Nl3aB7ut0fgMdLNrOfhoZxzkwOPdWvgcE0359xrwHeBkYHFuvHvoaFvPPSzZtbXObfSOfffNA+GNiiw7I7A4aWzgSPWKdJWOtQjEc05t8TMngGW03zIZWFg1s3AI2bmgJa3Y/wT8DczW0PzkMeraT4U05oFNB+6yQeeOPQHwcx+BMwxsyia7zd7O7DlGDGTgRet+abkBtwZmH4f8KyZVQLvAr0D078TKPemQL7XA4/xspmtBBYRfsM1SwTR6JziK4EzcLo452rMrC/wNjDQOVd32HI3AUXOuTs8iCkSVNrjF79JAN4LvFlqwDcOL32Rzk57/CIiPqM3d0VEfEbFLyLiMyp+ERGfUfGLiPiMil9ExGf+H9RoKE0xmZKpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "median:-18.737760472819794\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "median:0.687\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "median:0.5565\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "median:-511.34250000000003\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "median:1.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "median:4.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "median:11.0\n"
     ]
    }
   ],
   "source": [
    "graph1 = sns.histplot(df[df['total_score']<0]['sasa'], kde=True)\n",
    "graph1.axvline(df['sasa'].median(), color='red')\n",
    "plt.show()\n",
    "print('median:'+ str(df['sasa'].median()))\n",
    "\n",
    "graph3 = sns.histplot(df[df['total_score']<0]['ddG'], kde=True)\n",
    "graph3.axvline(df['ddG'].median(), color='red')\n",
    "plt.show()\n",
    "print('median:'+ str(df['ddG'].median()))\n",
    "\n",
    "graph4 = sns.histplot(df[df['total_score']<0]['ddg-per-sasa'], kde=True)\n",
    "graph4.axvline(df['ddg-per-sasa'].median(), color='red')\n",
    "plt.show()\n",
    "print('median:'+ str(df['ddg-per-sasa'].median()))\n",
    "\n",
    "graph5 = sns.histplot(df[df['total_score']<0]['sc'], kde=True)\n",
    "graph5.axvline(df['sc'].median(), color='red')\n",
    "plt.show()\n",
    "print('median:'+ str(df['sc'].median()))\n",
    "\n",
    "graph6 = sns.histplot(df[df['total_score']<0]['graft_RMSD'], kde=True)\n",
    "graph6.axvline(df['graft_RMSD'].median(), color='red')\n",
    "plt.show()\n",
    "print('median:'+ str(df['graft_RMSD'].median()))\n",
    "\n",
    "graph7 = sns.histplot(df[df['total_score']<0]['total_score'], kde=True)\n",
    "graph7.axvline(df['total_score'].median(), color='red')\n",
    "plt.show()\n",
    "print('median:'+ str(df['total_score'].median()))\n",
    "\n",
    "graph8 = sns.histplot(df[df['total_score']<0]['buried_unsat_hbonds'], kde=True)\n",
    "graph8.axvline(df['buried_unsat_hbonds'].median(), color='red')\n",
    "plt.show()\n",
    "print('median:'+ str(df['buried_unsat_hbonds'].median()))\n",
    "\n",
    "graph9 = sns.histplot(df[df['total_score']<0]['buried_unsat_hbonds2'], kde=True)\n",
    "graph9.axvline(df['buried_unsat_hbonds2'].median(), color='red')\n",
    "plt.show()\n",
    "print('median:'+ str(df['buried_unsat_hbonds2'].median()))\n",
    "\n",
    "graph10 = sns.histplot(df[df['total_score']<0]['hbonds'], kde=True)\n",
    "graph10.axvline(df['hbonds'].median(), color='red')\n",
    "plt.show()\n",
    "print('median:'+ str(df['hbonds'].median()))\n",
    "\n",
    "#graph11 = sns.histplot(df[df['total_score']<0]['af2rmsd'], kde=True)\n",
    "#graph11.axvline(df['af2rmsd'].median(), color='red')\n",
    "#plt.show()\n",
    "#print('median:'+ str(df['af2rmsd'].median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of designs: 68\n",
      "{1: 42, 2: 26}\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "totalseed=df['seed'].max()\n",
    "dico_nseed={}\n",
    "\n",
    "for i in range(1,totalseed+1,1):\n",
    "    subdf=df[df['seed']==i]\n",
    "    nseed=subdf.shape[0]\n",
    "    dico_nseed[i]=nseed\n",
    "print('Total number of designs: {}'.format(df.shape[0]))\n",
    "\n",
    "fig= plt.figure(figsize=(20,5))\n",
    "keys = list(dico_nseed.keys())\n",
    "vals = [dico_nseed[k] for k in keys]\n",
    "sns.barplot(x=keys, y = vals)\n",
    "plt.show\n",
    "\n",
    "print(dico_nseed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selection of best designs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The selection aims to obtain a similar amount of designs per seed, i.e. avoiding one grafted seeds over/under-representation. Therefore, different cutoffs can be set: For one specific seed, if too many designs are selected based on cutoff 1, then cutoff 2 applies, and so on until the number of designs is below the maximum allowed for one seed. However, if the number of designs falls under the minimum, then the previous cutoff is privileged. \n",
    "\n",
    "The cutoffs contains important metrics like the dSASA-normalized binding energy (ddG-per-SASA), the shape complementarity (SC), the number of buried unsatisfied polar atoms, the number of interface hydrogen bonds and the buried solvant-accessible surface area. It is advised to set the values empirically according to the number of designs you aim for."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting the cutoffs\n",
    "Please modify the values of the following block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cutoff1={'ddg-per-sasa': -21.5, \n",
    "         'sc': 0.67,\n",
    "         'buried_unsat_hbonds': 2, \n",
    "         'hbonds': 5,\n",
    "         'sasa': 1200\n",
    "         #,'af2rmsd': 1\n",
    "        }\n",
    "\n",
    "cutoff2={'ddg-per-sasa': -23, \n",
    "         'sc': 0.699,\n",
    "         'buried_unsat_hbonds': 1, \n",
    "         'hbonds': 7,\n",
    "         'sasa': 1270\n",
    "         #,'af2rmsd': 1\n",
    "        }\n",
    "\n",
    "cutoff3={'ddg-per-sasa': -25, \n",
    "         'sc': 0.71,\n",
    "         'buried_unsat_hbonds': 1, \n",
    "         'hbonds': 9,\n",
    "         'sasa': 1335\n",
    "         #,'af2rmsd': 1\n",
    "        }\n",
    "\n",
    "cutoff4={'ddg-per-sasa': -27.7, \n",
    "         'sc': 0.73,\n",
    "         'buried_unsat_hbonds': 1, \n",
    "         'hbonds': 10,\n",
    "         'sasa': 1370\n",
    "         #,'af2rmsd': 1\n",
    "        }\n",
    "\n",
    "cutoff5={'ddg-per-sasa': -30, \n",
    "         'sc': 0.72,\n",
    "         'buried_unsat_hbonds': 0, \n",
    "         'hbonds': 11,\n",
    "         'sasa': 1390\n",
    "         #,'af2rmsd': 1\n",
    "        }\n",
    "\n",
    "mindes=5\n",
    "maxdes=40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_preselection(dico_cutoff, df2, seedid):\n",
    "    preselect=df2[(df2['ddg-per-sasa']<int(dico_cutoff['ddg-per-sasa'])) &\n",
    "              (df2['sc']>float(dico_cutoff['sc'])) &\n",
    "              (df2['buried_unsat_hbonds']<=int(dico_cutoff['buried_unsat_hbonds'])) & \n",
    "              (df2['hbonds']>=int(dico_cutoff['hbonds'])) & \n",
    "              (df2['sasa']>=int(dico_cutoff['sasa'])) &\n",
    "              (df2['seed']==int(seedid))\n",
    "              # & (df2['af2rmsd']<=int(dico_cutoff['af2rmsd']))\n",
    "              ]\n",
    "    return preselect"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making the selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seed 1 : cutoff_1 ( 3 )\n",
      "Seed 2 : cutoff_1 ( 3 )\n",
      "\n",
      "Number of designs selected: 6\n",
      "\n",
      "{'None': 0, 'Not passed': 0, 'Cutoff 1': 2, 'Cutoff 2': 0, 'Cutoff 3': 0, 'Cutoff 4': 0, 'Cutoff 5': 0, 'Over': 0}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_score</th>\n",
       "      <th>buried_unsat_hbonds</th>\n",
       "      <th>buried_unsat_hbonds2</th>\n",
       "      <th>sasa</th>\n",
       "      <th>sc</th>\n",
       "      <th>hbonds</th>\n",
       "      <th>ddG</th>\n",
       "      <th>graft_RMSD</th>\n",
       "      <th>array</th>\n",
       "      <th>seed</th>\n",
       "      <th>description</th>\n",
       "      <th>ddg-per-sasa</th>\n",
       "      <th>hb-per-sasa</th>\n",
       "      <th>bunsh-per-sasa</th>\n",
       "      <th>bunsh2-per-sasa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-436.302</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1216.499</td>\n",
       "      <td>0.682</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-26.253</td>\n",
       "      <td>0.512</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>EEH_1.0_01_S1_0002</td>\n",
       "      <td>-21.580782</td>\n",
       "      <td>6.576249</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.466093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>-509.124</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1234.326</td>\n",
       "      <td>0.683</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-27.539</td>\n",
       "      <td>0.589</td>\n",
       "      <td>378</td>\n",
       "      <td>1</td>\n",
       "      <td>HEEH_rd4_0024_S1</td>\n",
       "      <td>-22.310962</td>\n",
       "      <td>7.291429</td>\n",
       "      <td>0.810159</td>\n",
       "      <td>4.050794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>-483.736</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1243.269</td>\n",
       "      <td>0.681</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-27.954</td>\n",
       "      <td>0.605</td>\n",
       "      <td>379</td>\n",
       "      <td>1</td>\n",
       "      <td>HEEH_rd4_0930_S1</td>\n",
       "      <td>-22.484273</td>\n",
       "      <td>6.434649</td>\n",
       "      <td>1.608662</td>\n",
       "      <td>3.217325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>-623.719</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1487.338</td>\n",
       "      <td>0.723</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-35.432</td>\n",
       "      <td>0.397</td>\n",
       "      <td>228</td>\n",
       "      <td>2</td>\n",
       "      <td>1CM2A_S2</td>\n",
       "      <td>-23.822426</td>\n",
       "      <td>8.740448</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.689369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>-653.412</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1331.050</td>\n",
       "      <td>0.680</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-28.061</td>\n",
       "      <td>0.419</td>\n",
       "      <td>303</td>\n",
       "      <td>2</td>\n",
       "      <td>4JRWA_S2</td>\n",
       "      <td>-21.081853</td>\n",
       "      <td>8.264152</td>\n",
       "      <td>0.751287</td>\n",
       "      <td>3.005146</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    total_score  buried_unsat_hbonds  buried_unsat_hbonds2      sasa     sc  \\\n",
       "1      -436.302                  0.0                   3.0  1216.499  0.682   \n",
       "36     -509.124                  1.0                   5.0  1234.326  0.683   \n",
       "41     -483.736                  2.0                   4.0  1243.269  0.681   \n",
       "50     -623.719                  0.0                   4.0  1487.338  0.723   \n",
       "60     -653.412                  1.0                   4.0  1331.050  0.680   \n",
       "\n",
       "    hbonds     ddG  graft_RMSD  array  seed         description  ddg-per-sasa  \\\n",
       "1      8.0 -26.253       0.512      2     1  EEH_1.0_01_S1_0002    -21.580782   \n",
       "36     9.0 -27.539       0.589    378     1    HEEH_rd4_0024_S1    -22.310962   \n",
       "41     8.0 -27.954       0.605    379     1    HEEH_rd4_0930_S1    -22.484273   \n",
       "50    13.0 -35.432       0.397    228     2            1CM2A_S2    -23.822426   \n",
       "60    11.0 -28.061       0.419    303     2            4JRWA_S2    -21.081853   \n",
       "\n",
       "    hb-per-sasa  bunsh-per-sasa  bunsh2-per-sasa  \n",
       "1      6.576249        0.000000         2.466093  \n",
       "36     7.291429        0.810159         4.050794  \n",
       "41     6.434649        1.608662         3.217325  \n",
       "50     8.740448        0.000000         2.689369  \n",
       "60     8.264152        0.751287         3.005146  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_select=pd.DataFrame([])\n",
    "cutoff_counts={'None':0,'Not passed':0, 'Cutoff 1':0, 'Cutoff 2':0, 'Cutoff 3':0,'Cutoff 4':0, 'Cutoff 5':0, 'Over':0}\n",
    "for i in range(1,totalseed+1,1):\n",
    "    preselect=df[df['seed']==i]\n",
    "    \n",
    "    if dico_nseed[i]==0:\n",
    "        print('Seed',i,' : no grafted design for this seed')\n",
    "        cutoff_counts['None']+=1\n",
    "        continue\n",
    "        \n",
    "    if dico_nseed[i]<mindes:\n",
    "        preselect=df[df['seed']==i]\n",
    "        final_select = pd.concat([final_select, preselect])\n",
    "        print('Seed',i,': too few designs => everything taken (',dico_nseed[i],')')\n",
    "        cutoff_counts['Not passed']+=1\n",
    "        continue\n",
    "        \n",
    "    previous_preselect=preselect\n",
    "    preselect=create_preselection(cutoff1, df, i)\n",
    "       \n",
    "    if (preselect.shape[0] <= maxdes):\n",
    "        final_select = pd.concat([final_select, preselect])\n",
    "        print('Seed',i, ': cutoff_1 (',preselect.shape[0],')')\n",
    "        cutoff_counts['Cutoff 1']+=1\n",
    "        if(preselect.shape[0]==0):\n",
    "            cutoff_counts['Not passed']+=1\n",
    "        continue\n",
    "    \n",
    "    previous_preselect=preselect    \n",
    "    preselect=create_preselection(cutoff2, df, i)\n",
    "       \n",
    "    if (preselect.shape[0]<=mindes):\n",
    "        final_select = pd.concat([final_select, previous_preselect])\n",
    "        print('Seed',i,': too few designs with cutoff_2 ==> cutoff_1 instead (',previous_preselect.shape[0],')')\n",
    "        cutoff_counts['Cutoff 1']+=1\n",
    "        continue\n",
    "    elif (preselect.shape[0]<=maxdes):\n",
    "        final_select = pd.concat([final_select, preselect])\n",
    "        print('Seed',i, ' : cutoff_2 (',preselect.shape[0],')')\n",
    "        cutoff_counts['Cutoff 2']+=1\n",
    "        continue\n",
    "    \n",
    "    previous_preselect=preselect\n",
    "    preselect=create_preselection(cutoff3, df, i)\n",
    "    \n",
    "    if (preselect.shape[0]<=mindes):\n",
    "        final_select = pd.concat([final_select, previous_preselect])\n",
    "        print('Seed',i,': too few designs with cutoff_3 ==> cutoff_2 instead (',previous_preselect.shape[0],')')\n",
    "        cutoff_counts['Cutoff 2']+=1\n",
    "        continue\n",
    "    elif (preselect.shape[0]<=maxdes):\n",
    "        final_select = pd.concat([final_select, preselect])\n",
    "        print('Seed',i, ' : cutoff_3 (',preselect.shape[0],')')\n",
    "        cutoff_counts['Cutoff 3']+=1\n",
    "        continue\n",
    "    \n",
    "    previous_preselect=preselect\n",
    "    preselect=create_preselection(cutoff4, df, i)\n",
    "    \n",
    "    if (preselect.shape[0]<=mindes):\n",
    "        final_select = pd.concat([final_select, previous_preselect])\n",
    "        print('Seed',i,': too few designs with cutoff_4 ==> cutoff_3 instead (',previous_preselect.shape[0],')')\n",
    "        cutoff_counts['Cutoff 3']+=1\n",
    "        continue\n",
    "    elif (preselect.shape[0]<=maxdes):\n",
    "        final_select = pd.concat([final_select, preselect])\n",
    "        print('Seed',i, ' : cutoff_4 (',preselect.shape[0],')')\n",
    "        cutoff_counts['Cutoff 4']+=1\n",
    "        continue\n",
    "    \n",
    "    previous_preselect=preselect\n",
    "    preselect=create_preselection(cutoff5, df, i)\n",
    "    \n",
    "    if (preselect.shape[0]<=mindes):\n",
    "        final_select = pd.concat([final_select, previous_preselect])\n",
    "        print('Seed',i,': too few designs with cutoff_5 ==> cutoff_4 instead (',previous_preselect.shape[0],')')\n",
    "        cutoff_counts['Cutoff 4']+=1\n",
    "        continue\n",
    "    elif (preselect.shape[0]<=maxdes):\n",
    "        final_select = pd.concat([final_select, preselect])\n",
    "        print('Seed',i, ' : cutoff_5 (',preselect.shape[0],')')\n",
    "        cutoff_counts['Cutoff 5']+=1\n",
    "        continue\n",
    "    else:\n",
    "        print('Seed',i,': too many designs with strictest cutoff (',previous_preselect.shape[0],') !')\n",
    "        cutoff_counts['Over']+=1\n",
    "    \n",
    "    print(preselect.shape[0]) \n",
    "        \n",
    "print('\\nNumber of designs selected: {}\\n'.format(final_select.shape[0]))\n",
    "\n",
    "select=final_select\n",
    "\n",
    "fig= plt.figure(figsize=(20,5))\n",
    "select_count=final_select['seed'].value_counts().to_frame().sort_index()\n",
    "sns.barplot(x=select_count.index, y=select_count['seed'])\n",
    "plt.show\n",
    "\n",
    "print(cutoff_counts)\n",
    "select.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Copying the selected files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cp ./out/EHEE_rd4_0464_S1.pdb ./out/EEEH_3.2_02_S1.pdb ./out/EHEE_rd4_0158_S1.pdb ./out/EHEE_rd4_0938_S1.pdb ./selected\n"
     ]
    }
   ],
   "source": [
    "cmd = r\"cp \"+ ''.join('./out/' + final_select['description'] + \".pdb \") + './selected'\n",
    "output = sp.run(cmd, shell=True, capture_output=True, text=True)\n",
    "print(cmd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
